Paper by Masanori Arita: “There are ethical, legal, and governance challenges surrounding data, particularly in the context of digital sequence information (DSI) on genetic resources. I focus on the shift in the international framework, as exemplified by the CBD-COP15 decision on benefit-sharing from DSI and discuss the growing significance of data sovereignty in the age of AI and synthetic biology. Using the example of the COVID-19 pandemic, the tension between open science principles and data control rights is explained. This opinion also highlights the importance of inclusive and equitable data sharing frameworks that respect both privacy and sovereign data rights, stressing the need for international cooperation and equitable access to data to reduce global inequalities in scientific and technological advancement…(More)”.
Organisations in the Age of Algorithms
Article by Phanish Puranam: “When Google’s CEO Sundar Pichai recently revealed that 25 percent of the company’s software is now machine-generated, it underscored how quickly artificial intelligence is reshaping the workplace.
What does this mean for how we organise and manage? Will there still be room for humans in tomorrow’s organisations? And what might their work conditions look like? I tackle these questions in my new book “Re-Humanize: How to Build Human-Centric Organizations in the Age of Algorithms”.
The answers are not a given. They will depend on what we choose to do – what kinds of organisations we design. I make the case that successful organisation designs will have to pursue both goal-centricity (i.e. achieving objectives) and human-centricity (i.e. creating social environments that people find attractive). A myopic focus on only one or the other will not bode well for us.
The dual purpose of organisations
Why focus on organisations at a time when technology seems to be making such exciting strides? This was the very first question that INSEAD alumna Joanna Gordon asked me in a recent digital@INSEAD webinar.
My answer: Homo sapiens’s most impressive accomplishments, from building the pyramids to developing Covid-19 vaccines, are not individual achievements. They were possible only because many people worked together effectively. “How to organise groups to attain goals” is our oldest general-purpose technology (GPT!).
But there is more. To humans, organisations don’t just help accomplish goals. We are a species that has evolved to survive and thrive in groups, and organisations (i.e. groups with goals) are the natural habitat of Homo sapiens. They provide us with a sense of community and, as research has shown, help us strike a balance between our needs for social connection, individual autonomy and feeling capable and effective…(More)”.
Critical Data Studies: An A to Z Guide to Concepts and Methods
Book by Rob Kitchin: “Critical Data Studies has come of age as a vibrant, interdisciplinary field of study. Taking data as its primary analytical focus, the field theorises the nature of data; examines how data are produced, managed, governed and shared; investigates how they are used to make sense of the world and to perform practical action; and explores whose agenda data-driven systems serve.
This book is the first comprehensive A-Z guide to the concepts and methods of Critical Data Studies, providing succinct definitions and descriptions of over 400 key terms, along with suggested further reading. The book enables readers to quickly navigate and improve their comprehension of the field, while also acting as a guide for discovering ideas and methods that will be of value in their own studies…(More)”
Introduction to the Foundations and Regulation of Generative AI
Chapter by Philipp Hacker, Andreas Engel, Sarah Hammer and Brent Mittelstadt: “… introduces The Oxford Handbook of the Foundations and Regulation of Generative AI, outlining the key themes and questions surrounding the technical development, regulatory governance, and societal implications of generative AI. It highlights the historical context of generative AI, distinguishes it from traditional AI, and explores its diverse applications across multiple domains, including text, images, music, and scientific discovery. The discussion critically assesses whether generative AI represents a paradigm shift or a temporary hype. Furthermore, the chapter extensively surveys both emerging and established regulatory frameworks, including the EU AI Act, the GDPR, privacy and personality rights, and copyright, as well as global legal responses. We conclude that, for now, the “Old Guard” of legal frameworks regulates generative AI more tightly and effectively than the “Newcomers,” but that may change as the new laws fully kick in. The chapter concludes by mapping the structure of the Handbook…(More)”
Reimagining the Policy Cycle in the Age of Artificial Intelligence
Paper by Sara Marcucci and Stefaan Verhulst: “The increasing complexity of global challenges, such as climate change, public health crises, and socioeconomic inequalities, underscores the need for a more sophisticated and adaptive policymaking approach. Evidence-Informed Decision-Making (EIDM) has emerged as a critical framework, leveraging data and research to guide policy design, implementation, and impact assessment. However, traditional evidence-based approaches, such as reliance on Randomized Controlled Trials (RCTs) and systematic reviews, face limitations, including resource intensity, contextual constraints, and difficulty in addressing real-time challenges. Artificial Intelligence offers transformative potential to enhance EIDM by enabling large-scale data analysis, pattern recognition, predictive modeling, and stakeholder engagement across the policy cycle. While generative AI has attracted significant attention, this paper emphasizes the broader spectrum of AI applications (beyond Generative AI) —such as natural language processing (NLP), decision trees, and basic machine learning algorithms—that continue to play a critical role in evidence-informed policymaking. These models, often more transparent and resource-efficient, remain highly relevant in supporting data analysis, policy simulations, and decision-support.
This paper explores AI’s role in three key phases of the policy cycle: (1) problem identification, where AI can support issue framing, trend detection, and scenario creation; (2) policy design, where AI-driven simulations and decision-support tools can improve solution alignment with real-world contexts; and (3) policy implementation and impact assessment, where AI can enhance monitoring, evaluation, and adaptive decision-making. Despite its promise, AI adoption in policymaking remains limited due to challenges such as algorithmic bias, lack of explainability, resource demands, and ethical concerns related to data privacy and environmental impact. To ensure responsible and effective AI integration, this paper highlights key recommendations: prioritizing augmentation over automation, embedding human oversight throughout AI-driven processes, facilitating policy iteration, and combining AI with participatory governance models…(More)”.
Gather, Share, Build
Article by Nithya Ramanathan & Jim Fruchterman: “Recent milestones in generative AI have sent nonprofits, social enterprises, and funders alike scrambling to understand how these innovations can be harnessed for global good. Along with this enthusiasm, there is also warranted concern that AI will greatly increase the digital divide and fail to improve the lives of 90 percent of the people on our planet. The current focus on funding AI intelligently and strategically in the social sector is critical, and it will help ensure that money has the largest impact.
So how can the social sector meet the current moment?
AI is already good at a lot of things. Plenty of social impact organizations are using AI right now, with positive results. Great resources exist for developing a useful understanding of the current landscape and how existing AI tech can serve your mission, including this report from Stanford HAI and Project Evident and this AI Treasure Map for Nonprofits from Tech Matters.
While some tech-for-good companies are creating AI and thriving—Digital Green, Khan Academy, and Jacaranda Health, among many—most social sector companies are not ready to build AI solutions. But even organizations that don’t have AI on their radar need to be thinking about how to address one of the biggest challenges to harnessing AI to solve social sector problems: insufficient data…(More)”.
Advanced Flood Hub features for aid organizations and govern
Announcement by Alex Diaz: “Floods continue to devastate communities worldwide, and many are pursuing advancements in AI-driven flood forecasting, enabling faster, more efficient detection and response. Over the past few years, Google Research has focused on harnessing AI modeling and satellite imagery to dramatically accelerate the reliability of flood forecasting — while working with partners to expand coverage for people in vulnerable communities around the world.
Today, we’re rolling out new advanced features in Flood Hub designed to allow experts to understand flood risk in a given region via inundation history maps, and to understand how a given flood forecast on Flood Hub might propagate throughout a river basin. With the inundation history maps, Flood Hub expert users can view flood risk areas in high resolution over the map regardless of a current flood event. This is useful for cases where our flood forecasting does not include real time inundation maps or for pre-planning of humanitarian work. You can find more explanations about the inundation history maps and more in the Flood Hub Help Center…(More)”.
Policymaking assessment framework
Guide by the Susan McKinnon Foundation: “This assessment tool supports the measurement of the quality of policymaking processes – both existing and planned – across sectors. It provides a flexible framework for rating public policy processes using information available in the public domain. The framework’s objective is to simplify the path towards best practice, evidence-informed policy.
It is intended to accommodate the complexity of policymaking processes and reflect the realities and context within which policymaking is undertaken. The criteria can be tailored for different policy problems and policy types and applied across sectors and levels of government.
The framework is structured around five key domains:
- understanding the problem
- engagement with stakeholders and partners
- outcomes focus
- evidence for the solution, and
- design and communication…(More)”.
What 40 Million Devices Can Teach Us About Digital Literacy in America
Blog by Juan M. Lavista Ferres: “…For the first time, Microsoft is releasing a privacy-protected dataset that provides new insights into digital engagement across the United States. This dataset, built from anonymized usage data from 40 million Windows devices, offers the most comprehensive view ever assembled of how digital tools are being used across the country. It goes beyond surveys and self-reported data to provide a real-world look at software application usage across 28,000 ZIP codes, creating a more detailed and nuanced understanding of digital engagement than any existing commercial or government study.
In collaboration with leading researchers at Harvard University and the University of Pennsylvania, we analyzed this dataset and developed two key indices to measure digital literacy:
- Media & Information Composite Index (MCI): This index captures general computing activity, including media consumption, information gathering, and usage of productivity applications like word processing, spreadsheets, and presentations.
- Content Creation & Computation Index (CCI): This index measures engagement with more specialized digital applications, such as content creation tools like Photoshop and software development environments.
By combining these indices with demographic data, several important insights emerge:
Urban-Rural Disparities Exist—But the Gaps Are Uneven While rural areas often lag in digital engagement, disparities within urban areas are just as pronounced. Some city neighborhoods have digital activity levels on par with major tech hubs, while others fall significantly behind, revealing a more complex digital divide than previously understood.
Income and Education Are Key Drivers of Digital Engagement Higher-income and higher-education areas show significantly greater engagement in content creation and computational tasks. This suggests that digital skills—not just access—are critical in shaping economic mobility and opportunity. Even in places where broadband availability is the same, digital usage patterns vary widely, demonstrating that access alone is not enough.
Infrastructure Alone Won’t Close the Digital Divide Providing broadband connectivity is essential, but it is not a sufficient solution to the challenges of digital literacy. Our findings show that even in well-connected regions, significant skill gaps persist. This means that policies and interventions must go beyond infrastructure investments to include comprehensive digital education, skills training, and workforce development initiatives…(More)”.
Patients’ Trust in Health Systems to Use Artificial Intelligence
Paper by Paige Nong and Jodyn Platt: “The growth and development of artificial intelligence (AI) in health care introduces a new set of questions about patient engagement and whether patients trust systems to use AI responsibly and safely. The answer to this question is embedded in patients’ experiences seeking care and trust in health systems. Meanwhile, the adoption of AI technology outpaces efforts to analyze patient perspectives, which are critical to designing trustworthy AI systems and ensuring patient-centered care.
We conducted a national survey of US adults to understand whether they trust their health systems to use AI responsibly and protect them from AI harms. We also examined variables that may be associated with these attitudes, including knowledge of AI, trust, and experiences of discrimination in health care….Most respondents reported low trust in their health care system to use AI responsibly (65.8%) and low trust that their health care system would make sure an AI tool would not harm them (57.7%)…(More)”.